diff --git a/404.html b/404.html
index 9efb35c..8138e04 100644
--- a/404.html
+++ b/404.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="https://sydneybiox.github.io/spicyR/index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/index.html b/articles/index.html
index 2185b0d..6e742b7 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/spicyR.html b/articles/spicyR.html
index 6453dff..baad83a 100644
--- a/articles/spicyR.html
+++ b/articles/spicyR.html
@@ -20,7 +20,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -541,7 +541,7 @@ <h2 id="references">References<a class="anchor" aria-label="anchor" href="#refer
 <span><span class="co">#&gt; [15] IRanges_2.40.0              S4Vectors_0.44.0           </span></span>
 <span><span class="co">#&gt; [17] BiocGenerics_0.52.0         MatrixGenerics_1.18.0      </span></span>
 <span><span class="co">#&gt; [19] matrixStats_1.4.1           ggplot2_3.5.1              </span></span>
-<span><span class="co">#&gt; [21] spicyR_1.19.1               BiocStyle_2.34.0           </span></span>
+<span><span class="co">#&gt; [21] spicyR_1.19.2               BiocStyle_2.34.0           </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; loaded via a namespace (and not attached):</span></span>
 <span><span class="co">#&gt;   [1] fs_1.6.5                    spatstat.sparse_3.1-0      </span></span>
diff --git a/authors.html b/authors.html
index 0add5c9..22cba07 100644
--- a/authors.html
+++ b/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/index.html b/index.html
index 4527891..a7d4933 100644
--- a/index.html
+++ b/index.html
@@ -22,7 +22,7 @@
 
     <a class="navbar-brand me-2" href="index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/pkgdown.yml b/pkgdown.yml
index a2f8ae9..5751161 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.1.1
 pkgdown_sha: ~
 articles:
   spicyR: spicyR.html
-last_built: 2024-11-14T00:02Z
+last_built: 2024-11-14T00:10Z
 urls:
   reference: https://sydneybiox.github.io/spicyR/reference
   article: https://sydneybiox.github.io/spicyR/articles
diff --git a/reference/bind.html b/reference/bind.html
index 5d0fd84..c9d5dea 100644
--- a/reference/bind.html
+++ b/reference/bind.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/colTest.html b/reference/colTest.html
index 1de17c9..7c2be31 100644
--- a/reference/colTest.html
+++ b/reference/colTest.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/convPairs.html b/reference/convPairs.html
index 415dadd..b995286 100644
--- a/reference/convPairs.html
+++ b/reference/convPairs.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/diabetesData.html b/reference/diabetesData.html
index ee3ad27..786e107 100644
--- a/reference/diabetesData.html
+++ b/reference/diabetesData.html
@@ -15,7 +15,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/getPairwise.html b/reference/getPairwise.html
index 13c2c7c..f6eda34 100644
--- a/reference/getPairwise.html
+++ b/reference/getPairwise.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/getProp.html b/reference/getProp.html
index 4edc8ee..8904f41 100644
--- a/reference/getProp.html
+++ b/reference/getProp.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index ece0f63..c48758b 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/signifPlot.html b/reference/signifPlot.html
index 6d80ce2..279af02 100644
--- a/reference/signifPlot.html
+++ b/reference/signifPlot.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/spicy.html b/reference/spicy.html
index a41afdb..c2edb11 100644
--- a/reference/spicy.html
+++ b/reference/spicy.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/spicyBoxPlot.html b/reference/spicyBoxPlot.html
index fba38ab..ea788e5 100644
--- a/reference/spicyBoxPlot.html
+++ b/reference/spicyBoxPlot.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/spicyTest.html b/reference/spicyTest.html
index 64d9dc2..a145076 100644
--- a/reference/spicyTest.html
+++ b/reference/spicyTest.html
@@ -13,7 +13,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/survBubble.html b/reference/survBubble.html
index c1a550f..60c404f 100644
--- a/reference/survBubble.html
+++ b/reference/survBubble.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/topPairs.html b/reference/topPairs.html
index f18f04d..41712bd 100644
--- a/reference/topPairs.html
+++ b/reference/topPairs.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">spicyR</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.1</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.19.2</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index 8815eff..66ebd92 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"","code":"if (!require(\"BiocManager\")) {   install.packages(\"BiocManager\") } BiocManager::install(\"spicyR\") # load required packages library(spicyR) library(ggplot2) library(SpatialExperiment) library(SpatialDatasets) library(imcRtools) library(dplyr) library(survival)"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"guide provides step--step instructions apply linear model multiple segmented labelled images assess localisation different cell types changes across different disease conditions.","code":""},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"use Keren et al. (2018) breast cancer dataset compare spatial distribution immune cells individuals different levels tumour infiltration (cold compartmentalised). data stored SpatialExperiment object contains single-cell spatial data 41 images. cell types dataset includes 11 immune cell types (double negative CD3 T cells, CD4 T cells, B cells, monocytes, macrophages, CD8 T cells, neutrophils, natural killer cells, dendritic cells, regulatory T cells), 2 structural cell types (endothelial, mesenchymal), 2 tumour cell types (keratin+ tumour, tumour) one unidentified category.","code":"kerenSPE <- SpatialDatasets::spe_Keren_2018()"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"linear-modelling","dir":"Articles","previous_headings":"","what":"Linear modelling","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"investigate changes localisation two different cell types, measure level localisation two cell types modelling L-function. L-function variance-stabilised K-function given equation Liĵ(r)=Kiĵ(r)π \\widehat{L_{ij}} (r) = \\sqrt{\\frac{\\widehat{K_{ij}}(r)}{\\pi}} Kiĵ\\widehat{K_{ij}} defined Kiĵ(r)=|W|ninj∑ni∑nj1{dij≤r}eij(r) \\widehat{K_{ij}} (r) = \\frac{|W|}{n_i n_j} \\sum_{n_i} \\sum_{n_j} 1 \\{d_{ij} \\leq r \\} e_{ij} (r) Kiĵ\\widehat{K_{ij}} summarises degree co-localisation cell type jj cell type ii, nin_i njn_j number cells type ii jj, |W||W| image area, dijd_{ij} distance two cells eij(r)e_{ij} (r) edge correcting factor. Specifically, mean difference experimental function theoretical function used measure level localisation, defined u=∑r′=rminrmaxL̂ij,Experimental(r′)−L̂ij,Poisson(r′) u = \\sum_{r' = r_{\\text{min}}}^{r_{\\text{max}}} \\widehat L_{ij, \\text{Experimental}} (r') - \\widehat L_{ij, \\text{Poisson}} (r') uu sum taken discrete range rr rminr_{\\text{min}} rmaxr_{\\text{max}}. Differences statistic uu two conditions modelled using weighted linear model.","code":""},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"test-for-change-in-localisation-for-a-specific-pair-of-cells","dir":"Articles","previous_headings":"Linear modelling","what":"Test for change in localisation for a specific pair of cells","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"Firstly, can test whether one cell type tends localised another cell type one condition compared . can done using spicy() function, specify condition parameter. example, want see whether neutrophils () tend found around CD8 T cells () compartmentalised tumours compared cold tumours. Given 3 conditions, can specify desired conditions setting order condition factor. spicy choose first level factor base condition second level comparison condition. spicy also naturally coerce condition column factor already factor. column containing cell type annotations can specified using cellType argument. default, spicy uses column named cellType SpatialExperiment object. obtain spicy object details results modelling performed. topPairs() function can used obtain associated coefficients p-value. coefficient spicyTestPair positive, find neutrophils significantly likely found near CD8 T cells compartmentalised tumours group compared cold tumour group.","code":"spicyTestPair <- spicy(   kerenSPE,   condition = \"tumour_type\",   from = \"CD8_T_cell\",   to = \"Neutrophils\" )  topPairs(spicyTestPair) #>                         intercept coefficient      p.value   adj.pvalue #> CD8_T_cell__Neutrophils  -109.081    112.0185 2.166646e-05 2.166646e-05 #>                               from          to #> CD8_T_cell__Neutrophils CD8_T_cell Neutrophils"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"test-for-change-in-localisation-for-all-pairwise-cell-combinations","dir":"Articles","previous_headings":"Linear modelling","what":"Test for change in localisation for all pairwise cell combinations","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"can perform pairwise combinations cell types excluding parameters spicy(). , obtain spicy object outlines result linear models performed pairwise combination cell types. can also examine L-function metrics individual images using convenient bind() function spicyTest results object. results can represented bubble plot using signifPlot() function.  , can observe significant relationships occur macrophages double negative CD3 T cells, suggesting two cell types far dispersed compartmentalised tumours compared cold tumours. examine specific cell type-cell type relationship detail, can use spicyBoxplot() specify either = \"Macrophages\" = \"dn_T_CD3\" rank = 1.","code":"spicyTest <- spicy(   kerenSPE,   condition = \"tumour_type\" )  topPairs(spicyTest) #>                             intercept coefficient      p.value   adj.pvalue #> Macrophages__dn_T_CD3       56.446064   -50.08474 1.080273e-07 3.035568e-05 #> dn_T_CD3__Macrophages       54.987151   -48.38664 2.194018e-07 3.082595e-05 #> Macrophages__DC_or_Mono     73.239404   -59.90361 5.224660e-06 4.893765e-04 #> DC_or_Mono__Macrophages     71.777087   -58.46833 7.431172e-06 5.220399e-04 #> dn_T_CD3__dn_T_CD3         -63.786032   100.61010 2.878804e-05 1.208706e-03 #> Neutrophils__dn_T_CD3      -63.141840    69.64356 2.891872e-05 1.208706e-03 #> dn_T_CD3__Neutrophils      -63.133725    70.15508 3.011012e-05 1.208706e-03 #> DC__Macrophages             96.893239   -92.55112 1.801300e-04 5.758129e-03 #> Macrophages__DC             96.896215   -93.25194 1.844241e-04 5.758129e-03 #> CD4_T_cell__Keratin_Tumour  -4.845037   -22.14995 2.834659e-04 7.409016e-03 #>                                   from             to #> Macrophages__dn_T_CD3      Macrophages       dn_T_CD3 #> dn_T_CD3__Macrophages         dn_T_CD3    Macrophages #> Macrophages__DC_or_Mono    Macrophages     DC_or_Mono #> DC_or_Mono__Macrophages     DC_or_Mono    Macrophages #> dn_T_CD3__dn_T_CD3            dn_T_CD3       dn_T_CD3 #> Neutrophils__dn_T_CD3      Neutrophils       dn_T_CD3 #> dn_T_CD3__Neutrophils         dn_T_CD3    Neutrophils #> DC__Macrophages                     DC    Macrophages #> Macrophages__DC            Macrophages             DC #> CD4_T_cell__Keratin_Tumour  CD4_T_cell Keratin_Tumour bind(spicyTest)[1:5, 1:5] #>   imageID         condition Keratin_Tumour__Keratin_Tumour #> 1       1             mixed                      -2.300602 #> 2       2             mixed                      -1.989699 #> 3       3 compartmentalised                      11.373530 #> 4       4 compartmentalised                      33.931133 #> 5       5 compartmentalised                      17.922818 #>   dn_T_CD3__Keratin_Tumour B_cell__Keratin_Tumour #> 1                -5.298543             -20.827279 #> 2               -16.020022               3.025815 #> 3               -21.857447             -24.962913 #> 4               -36.438476             -40.470221 #> 5               -20.816783             -38.138076 signifPlot(   spicyTest,   breaks = c(-3, 3, 1),   marksToPlot = c(\"Macrophages\", \"DC_or_Mono\", \"dn_T_CD3\", \"Neutrophils\",                   \"CD8_T_cell\", \"Keratin_Tumour\") ) spicyBoxPlot(results = spicyTest,               # from = \"Macrophages\",              # to = \"dn_T_CD3\"              rank = 1)"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"linear-modelling-for-custom-metrics","dir":"Articles","previous_headings":"","what":"Linear modelling for custom metrics","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicyR can also applied custom distance abundance metrics. kNN interactions graph can generated function buildSpatialGraph imcRtools package. generates colPairs object inside SpatialExperiment object. spicyR provides function convPairs converting colPairs object abundance matrix calculating average number nearby cells types every cell type given k. example, exists average 5 neutrophils every macrophage image 1, column Neutrophil__Macrophage value 5 image 1. custom distance abundance metrics can included analysis alternateResult parameter. Statial package contains custom distance metrics can used spicy.","code":"kerenSPE <- imcRtools::buildSpatialGraph(kerenSPE,                                           img_id = \"imageID\",                                           type = \"knn\", k = 20,                                         coords = c(\"x\", \"y\"))  pairAbundances <- convPairs(kerenSPE,                   colPair = \"knn_interaction_graph\")  head(pairAbundances[\"B_cell__B_cell\"]) #>    B_cell__B_cell #> 1      12.7349608 #> 10      0.2777778 #> 11      0.0000000 #> 12      1.3333333 #> 13      1.2200957 #> 14      0.0000000 spicyTestColPairs <- spicy(   kerenSPE,   condition = \"tumour_type\",   alternateResult = pairAbundances,   weights = FALSE )  topPairs(spicyTestColPairs) #>                            intercept coefficient     p.value adj.pvalue #> CD8_T_cell__Neutrophils  0.833333333  -0.7592968 0.002645466  0.3291833 #> B_cell__Tumour           0.001937984   0.2602822 0.004872664  0.3291833 #> Other_Immune__NK         0.012698413   0.2612881 0.005673068  0.3291833 #> Unidentified__CD8_T_cell 0.106626794   0.6387339 0.005906526  0.3291833 #> dn_T_CD3__NK             0.004242424   0.2148797 0.006317829  0.3291833 #> CD4_T_cell__Neutrophils  0.036213602   0.2947696 0.007902670  0.3291833 #> Tregs__CD4_T_cell        0.128876212   0.5726201 0.010207087  0.3291833 #> Endothelial__DC          0.008771930   0.3008523 0.011189533  0.3291833 #> Tumour__Neutrophils      0.021638939   0.2529045 0.011388850  0.3291833 #> Mesenchymal__Neutrophils 0.004504505   0.2494301 0.012761315  0.3291833 #>                                  from          to #> CD8_T_cell__Neutrophils    CD8_T_cell Neutrophils #> B_cell__Tumour                 B_cell      Tumour #> Other_Immune__NK         Other_Immune          NK #> Unidentified__CD8_T_cell Unidentified  CD8_T_cell #> dn_T_CD3__NK                 dn_T_CD3          NK #> CD4_T_cell__Neutrophils    CD4_T_cell Neutrophils #> Tregs__CD4_T_cell               Tregs  CD4_T_cell #> Endothelial__DC           Endothelial          DC #> Tumour__Neutrophils            Tumour Neutrophils #> Mesenchymal__Neutrophils  Mesenchymal Neutrophils signifPlot(   spicyTestColPairs,   breaks = c(-3, 3, 1),   marksToPlot = c(\"Macrophages\", \"dn_T_CD3\", \"CD4_T_cell\",                    \"B_cell\", \"DC_or_Mono\", \"Neutrophils\", \"CD8_T_cell\") )"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"performing-survival-analysis","dir":"Articles","previous_headings":"","what":"Performing survival analysis","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicy can also used perform survival analysis asses whether changes co-localisation cell types associated survival probability. spicy requires SingleCellExperiment object used contain column called survival Surv object. can perform survival analysis using spicy function specifying condition = \"survival\". can access corresponding coefficients p-values accessing survivalResults slot spicy results object.","code":"kerenSPE$event = 1 - kerenSPE$Censored kerenSPE$survival = Surv(kerenSPE$`Survival_days_capped*`, kerenSPE$event) # Running survival analysis spicySurvival = spicy(kerenSPE,                       condition = \"survival\")  # top 10 significant pairs head(spicySurvival$survivalResults, 10) #> # A tibble: 10 × 4 #>    test                       coef se.coef    p.value #>    <chr>                     <dbl>   <dbl>      <dbl> #>  1 Other_Immune__Tregs     0.0236  0.00866 0.00000893 #>  2 CD4_T_cell__Tregs       0.0177  0.00685 0.0000124  #>  3 Tregs__Other_Immune     0.0237  0.00873 0.0000126  #>  4 Tregs__CD4_T_cell       0.0171  0.00676 0.0000285  #>  5 CD8_T_cell__CD8_T_cell  0.00605 0.00272 0.000332   #>  6 Tumour__CD8_T_cell     -0.0305  0.0114  0.000617   #>  7 CD8_T_cell__Tumour     -0.0305  0.0116  0.000721   #>  8 CD4_T_cell__dn_T_CD3    0.00845 0.00353 0.000794   #>  9 dn_T_CD3__CD4_T_cell    0.00840 0.00353 0.000937   #> 10 DC__Other_Immune       -0.0289  0.0123  0.00103"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"accounting-for-tissue-inhomogeneity","dir":"Articles","previous_headings":"","what":"Accounting for tissue inhomogeneity","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicy function can also account tissue inhomogeneity avoid false positives negatives. can done setting sigma = parameter within spicy function. default, sigma set NULL, spicy assumes homogeneous tissue structure. example, examine L-function Keratin_Tumour__Neutrophils sigma = NULL Rs = 100, value positive, indicating attraction two cell types. specify sigma = 20 re-calculate L-function, indicates relationship Keratin_Tumour Neutrophils, .e., major attraction dispersion, now takes account tissue inhomogeneity.  Plotting image 24 shows supposed co-localisation occurs due dense cluster cells near top image.","code":"# filter SPE object to obtain image 24 data kerenSubset = kerenSPE[, colData(kerenSPE)$imageID == \"24\"]  pairwiseAssoc = getPairwise(kerenSubset,                              sigma = NULL,                              Rs = 100) |>   as.data.frame()  pairwiseAssoc[[\"Keratin_Tumour__Neutrophils\"]] #> [1] 10.88892 pairwiseAssoc = getPairwise(kerenSubset,                              sigma = 20,                              Rs = 100) |>   as.data.frame()  pairwiseAssoc[[\"Keratin_Tumour__Neutrophils\"]] #> [1] 0.9024836 # obtain colData for image 24 cData = colData(kerenSPE) |> as.data.frame() |>            dplyr::filter(imageID == \"24\")  # obtain cells present in image 24 coords = spatialCoords(kerenSPE) |> as.data.frame() coords$cellID = rownames(coords) coords = coords |> dplyr::filter(cellID %in% cData$CellID)  cData$X = coords$x cData$Y = coords$y  cData = cData |>    dplyr::mutate(cellTypeNew = ifelse(cellType %in% c(\"Keratin_Tumour\", \"Neutrophils\"),                                       cellType, \"Other\"))   pal = setNames(c(\"#d6b11c\", \"#850f07\"),                  c(\"Keratin_Tumour\", \"Neutrophils\"))  ggplot() +     stat_density_2d(data = cData, aes(x = X, y = Y, fill = after_stat(density)),                      geom = \"raster\",                      contour = FALSE) +     geom_point(data = cData |> filter(cellType != \"Other\"),                aes(x = X, y = Y, colour = cellTypeNew), size = 1) +     scale_color_manual(values = pal) +     scale_fill_distiller(palette = \"Blues\", direction = 1) +     theme_classic() +     labs(title = \"image ID: 24\")"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"mixed-effects-modelling","dir":"Articles","previous_headings":"","what":"Mixed effects modelling","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicyR supports mixed effects modelling multiple images obtained subject. case, subject treated random effect condition treated fixed effect. perform mixed effects modelling, can specify subject parameter spicy function.","code":"spicyMixedTest <- spicy(   diabetesData,   condition = \"stage\",   subject = \"case\" )"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"","code":"sessionInfo() #> R version 4.4.2 (2024-10-31) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.5 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 #>  #> locale: #>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        #>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    #>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           #> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    #>  #> time zone: UTC #> tzcode source: system (glibc) #>  #> attached base packages: #> [1] stats4    stats     graphics  grDevices utils     datasets  methods   #> [8] base      #>  #> other attached packages: #>  [1] survival_3.7-0              dplyr_1.1.4                 #>  [3] imcRtools_1.12.0            SpatialDatasets_1.4.0       #>  [5] ExperimentHub_2.14.0        AnnotationHub_3.14.0        #>  [7] BiocFileCache_2.14.0        dbplyr_2.5.0                #>  [9] SpatialExperiment_1.16.0    SingleCellExperiment_1.28.1 #> [11] SummarizedExperiment_1.36.0 Biobase_2.66.0              #> [13] GenomicRanges_1.58.0        GenomeInfoDb_1.42.0         #> [15] IRanges_2.40.0              S4Vectors_0.44.0            #> [17] BiocGenerics_0.52.0         MatrixGenerics_1.18.0       #> [19] matrixStats_1.4.1           ggplot2_3.5.1               #> [21] spicyR_1.19.1               BiocStyle_2.34.0            #>  #> loaded via a namespace (and not attached): #>   [1] fs_1.6.5                    spatstat.sparse_3.1-0       #>   [3] bitops_1.0-9                sf_1.0-19                   #>   [5] EBImage_4.48.0              httr_1.4.7                  #>   [7] RColorBrewer_1.1-3          numDeriv_2016.8-1.1         #>   [9] tools_4.4.2                 backports_1.5.0             #>  [11] utf8_1.2.4                  R6_2.5.1                    #>  [13] DT_0.33                     HDF5Array_1.34.0            #>  [15] mgcv_1.9-1                  rhdf5filters_1.18.0         #>  [17] withr_3.0.2                 sp_2.1-4                    #>  [19] gridExtra_2.3               coxme_2.2-22                #>  [21] ClassifyR_3.10.0            cli_3.6.3                   #>  [23] textshaping_0.4.0           spatstat.explore_3.3-3      #>  [25] labeling_0.4.3              sass_0.4.9                  #>  [27] nnls_1.6                    spatstat.data_3.1-2         #>  [29] readr_2.1.5                 proxy_0.4-27                #>  [31] pkgdown_2.1.1               systemfonts_1.1.0           #>  [33] ggupset_0.4.0               svglite_2.1.3               #>  [35] RSQLite_2.3.7               generics_0.1.3              #>  [37] vroom_1.6.5                 spatstat.random_3.3-2       #>  [39] car_3.1-3                   scam_1.2-17                 #>  [41] Matrix_1.7-1                ggbeeswarm_0.7.2            #>  [43] fansi_1.0.6                 abind_1.4-8                 #>  [45] terra_1.7-83                lifecycle_1.0.4             #>  [47] yaml_2.3.10                 carData_3.0-5               #>  [49] rhdf5_2.50.0                SparseArray_1.6.0           #>  [51] grid_4.4.2                  blob_1.2.4                  #>  [53] promises_1.3.0              crayon_1.5.3                #>  [55] bdsmatrix_1.3-7             shinydashboard_0.7.2        #>  [57] lattice_0.22-6              beachmat_2.22.0             #>  [59] KEGGREST_1.46.0             magick_2.8.5                #>  [61] cytomapper_1.18.0           pillar_1.9.0                #>  [63] knitr_1.49                  RTriangle_1.6-0.14          #>  [65] rjson_0.2.23                boot_1.3-31                 #>  [67] codetools_0.2-20            glue_1.8.0                  #>  [69] spatstat.univar_3.1-1       data.table_1.16.2           #>  [71] MultiAssayExperiment_1.32.0 vctrs_0.6.5                 #>  [73] png_0.1-8                   gtable_0.3.6                #>  [75] cachem_1.1.0                xfun_0.49                   #>  [77] S4Arrays_1.6.0              mime_0.12                   #>  [79] tidygraph_1.3.1             pheatmap_1.0.12             #>  [81] units_0.8-5                 nlme_3.1-166                #>  [83] bit64_4.5.2                 filelock_1.0.3              #>  [85] bslib_0.8.0                 svgPanZoom_0.3.4            #>  [87] KernSmooth_2.23-24          vipor_0.4.7                 #>  [89] colorspace_2.1-1            DBI_1.2.3                   #>  [91] raster_3.6-30               tidyselect_1.2.1            #>  [93] bit_4.5.0                   compiler_4.4.2              #>  [95] curl_6.0.0                  BiocNeighbors_2.0.0         #>  [97] desc_1.4.3                  DelayedArray_0.32.0         #>  [99] bookdown_0.41               scales_1.3.0                #> [101] classInt_0.4-10             distances_0.1.11            #> [103] rappdirs_0.3.3              tiff_0.1-12                 #> [105] stringr_1.5.1               digest_0.6.37               #> [107] goftest_1.2-3               fftwtools_0.9-11            #> [109] spatstat.utils_3.1-1        minqa_1.2.8                 #> [111] rmarkdown_2.29              XVector_0.46.0              #> [113] htmltools_0.5.8.1           pkgconfig_2.0.3             #> [115] jpeg_0.1-10                 lme4_1.1-35.5               #> [117] fastmap_1.2.0               rlang_1.1.4                 #> [119] htmlwidgets_1.6.4           ggthemes_5.1.0              #> [121] UCSC.utils_1.2.0            shiny_1.9.1                 #> [123] ggh4x_0.2.8                 farver_2.1.2                #> [125] jquerylib_0.1.4             jsonlite_1.8.9              #> [127] BiocParallel_1.40.0         RCurl_1.98-1.16             #> [129] magrittr_2.0.3              scuttle_1.16.0              #> [131] Formula_1.2-5               GenomeInfoDbData_1.2.13     #> [133] Rhdf5lib_1.28.0             munsell_0.5.1               #> [135] Rcpp_1.0.13-1               ggnewscale_0.5.0            #> [137] viridis_0.6.5               stringi_1.8.4               #> [139] ggraph_2.2.1                zlibbioc_1.52.0             #> [141] MASS_7.3-61                 plyr_1.8.9                  #> [143] parallel_4.4.2              ggrepel_0.9.6               #> [145] deldir_2.0-4                Biostrings_2.74.0           #> [147] graphlayouts_1.2.0          splines_4.4.2               #> [149] tensor_1.5                  hms_1.1.3                   #> [151] locfit_1.5-9.10             igraph_2.1.1                #> [153] ggpubr_0.6.0                spatstat.geom_3.3-3         #> [155] ggsignif_0.6.4              reshape2_1.4.4              #> [157] BiocVersion_3.20.0          evaluate_1.0.1              #> [159] BiocManager_1.30.25         tzdb_0.4.0                  #> [161] nloptr_2.1.1                tweenr_2.0.3                #> [163] httpuv_1.6.15               tidyr_1.3.1                 #> [165] purrr_1.0.2                 polyclip_1.10-7             #> [167] ggforce_0.4.2               broom_1.0.7                 #> [169] xtable_1.8-4                e1071_1.7-16                #> [171] rstatix_0.7.2               later_1.3.2                 #> [173] class_7.3-22                viridisLite_0.4.2           #> [175] ragg_1.3.3                  tibble_3.2.1                #> [177] lmerTest_3.1-3              memoise_2.0.1               #> [179] beeswarm_0.4.0              AnnotationDbi_1.68.0        #> [181] concaveman_1.1.0"},{"path":"https://sydneybiox.github.io/spicyR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Nicolas Canete. Author. Ellis Patrick. Author, maintainer. Nicholas Robertson. Contributor. Alex Qin. Contributor. Farhan Ameen. Contributor. Shreya Rao. Contributor.","code":""},{"path":"https://sydneybiox.github.io/spicyR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Canete N, Iyengar S, Ormerod J, Baharlou H, Harman , Patrick E (2022). “spicyR: spatial analysis situ cytometry data R.” Bioinformatics, 38(11), 3099–3105. doi:10.1093/bioinformatics/btac268, https://doi.org/10.1093/bioinformatics/btac268.","code":"@Article{,   title = {{spicyR: spatial analysis of in situ cytometry data in R}},   author = {Nicolas Canete and Sourish Iyengar and John Ormerod and Heeva Baharlou and Andrew Harman and Ellis Patrick},   year = {2022},   volume = {38},   number = {11},   pages = {3099–3105},   journal = {Bioinformatics},   doi = {10.1093/bioinformatics/btac268},   url = {https://doi.org/10.1093/bioinformatics/btac268}, }"},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"spicyr","dir":"","previous_headings":"","what":"Spatial analysis of in situ cytometry data","title":"Spatial analysis of in situ cytometry data","text":"Spatial analysis situ cytometry data.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Spatial analysis of in situ cytometry data","text":"spicyR package provides framework performing inference changes spatial relationships pairs cell types cell-resolution spatial omics technologies. spicyR consists three primary steps: () summarizing degree spatial localization pairs cell types image; (ii) modelling variability localization summary statistics function cell counts (iii) testing changes spatial localizations associated response variable.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Spatial analysis of in situ cytometry data","text":"Bioconductor release version, run following. like --date features, install recent development version.","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"spicyR\") # Install the development version from Bioconductor: if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # The following initializes usage of Bioc devel # This will update all your Bioconductor packages to devel version BiocManager::install(version='devel')  BiocManager::install(\"spicyR\")  # Otherwise install the development version from GitHub: # install.packages(\"devtools\") devtools::install_github(\"ellispatrick/spicyR\") library(spicyR)"},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"submitting-an-issue-or-feature-request","dir":"","previous_headings":"","what":"Submitting an issue or feature request","title":"Spatial analysis of in situ cytometry data","text":"spicyR still active development. greatly appreciate feedback related package. R package related issues raised . general questions feedback, please contact us directly via ellis.patrick@sydney.edu.au.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Spatial analysis of in situ cytometry data","text":"Nicolas Canete Ellis Patrick - @TheEllisPatrick","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Spatial analysis of in situ cytometry data","text":"Nicolas P Canete, Sourish S Iyengar, John T Ormerod, Heeva Baharlou, Andrew N Harman, Ellis Patrick, spicyR: spatial analysis situ cytometry data R, Bioinformatics, Volume 38, Issue 11, 1 June 2022, Pages 3099–3105, https://doi.org/10.1093/bioinformatics/btac268","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":null,"dir":"Reference","previous_headings":"","what":"Produces a dataframe showing L-function metric for each imageID entry. — bind","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"Produces dataframe showing L-function metric imageID entry.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"","code":"bind(results, pairName = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"results Spicy test result obtained spicy. pairName string specifying pairwise interaction interest. NULL, pairwise interactions shown.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"data.frame containing colData related results.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"","code":"data(spicyTest) df <- bind(spicyTest)"},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"Perform simple wilcoxon-rank-sum test t-test columns data frame","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"","code":"colTest(df, condition, type = NULL, feature = NULL, imageID = \"imageID\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"df data.frame SingleCellExperiment, SpatialExperiment condition condition interest type type test, \"wilcox\", \"ttest\" \"survival\". feature Can used calculate proportions feature image imageID imageID's presenting SingleCellExperiment","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"Proportions","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"","code":"# Test for an association with long-duration diabetes # This is clearly ignoring the repeated measures... data(\"diabetesData\") diabetesData <- spicyR:::.format_data(   diabetesData, \"imageID\", \"cellType\", c(\"x\", \"y\"), FALSE ) props <- getProp(diabetesData) condition <- spicyR:::getImagePheno(diabetesData)$stage names(condition) <- spicyR:::getImagePheno(diabetesData)$imageID condition <- condition[condition %in% c(\"Long-duration\", \"Onset\")] test <- colTest(props[names(condition), ], condition)"},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"Converts colPairs object abundance matrix based number nearby interactions every cell type.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"","code":"convPairs(cells, colPair, imageID = \"imageID\", cellType = \"cellType\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"cells SingleCellExperiment contains objects colPairs slot. colPair name object colPairs slot dataframe constructed . imageID image ID using SingleCellExperiment. cellType cell type using SingleCellExperiment.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"Matrix abundances","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"","code":"data(\"diabetesData\") images <- c(\"A09\", \"A11\", \"A16\", \"A17\") diabetesData <- diabetesData[   , SummarizedExperiment::colData(diabetesData)$imageID %in% images ]  diabetesData_SPE <- SpatialExperiment::SpatialExperiment(diabetesData,   colData = SummarizedExperiment::colData(diabetesData) ) SpatialExperiment::spatialCoords(diabetesData_SPE) <- data.frame(   SummarizedExperiment::colData(diabetesData_SPE)$x,   SummarizedExperiment::colData(diabetesData_SPE)$y ) |>   as.matrix()  SpatialExperiment::spatialCoordsNames(diabetesData_SPE) <- c(\"x\", \"y\")  diabetesData_SPE <- imcRtools::buildSpatialGraph(diabetesData_SPE,   img_id = \"imageID\",   type = \"knn\",   k = 20,   coords = c(\"x\", \"y\") ) #> 'sample_id's are duplicated across 'SpatialExperiment' objects to cbind; appending sample indices. #> The returned object is ordered by the 'imageID' entry.  pairAbundances <- convPairs(diabetesData_SPE,   colPair = \"knn_interaction_graph\" )"},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":null,"dir":"Reference","previous_headings":"","what":"Diabetes IMC data in SCE format. — diabetesData","title":"Diabetes IMC data in SCE format. — diabetesData","text":"subset Damond et al 2019 imaging mass cytometry dataset. data contains cells pancreatic islets individuals early onset diabetes healthy controls. object contains single-cell data 160 images 8 subjects, 20 images per subject.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diabetes IMC data in SCE format. — diabetesData","text":"","code":"data(\"diabetesData\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diabetes IMC data in SCE format. — diabetesData","text":"diabetesData_SCE SingleCellExperiment object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diabetes IMC data in SCE format. — diabetesData","text":"Converted SingleCellExperiment format.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get statistic from pairwise L curve of a single image. — getPairwise","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"Get statistic pairwise L curve single image.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"","code":"getPairwise(   cells,   imageID = \"imageID\",   cellType = \"cellType\",   spatialCoords = c(\"x\", \"y\"),   Rs = NULL,   sigma = NULL,   from = NULL,   to = NULL,   cores = 1,   minLambda = 0.05,   window = \"convex\",   window.length = NULL,   edgeCorrect = TRUE,   includeZeroCells = FALSE,   BPPARAM = BiocParallel::SerialParam(),   imageIDCol = imageID,   cellTypeCol = cellType,   spatialCoordCols = spatialCoords,   nCores = cores )"},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"cells SummarizedExperiment contains least variables x y, giving location coordinates cell, cellType. imageID name imageID column using SingleCellExperiment SpatialExperiment. cellType name cellType column using SingleCellExperiment SpatialExperiment. spatialCoords names spatialCoords column using SingleCellExperiment. Rs vector radii measures association calculated . sigma numeric variable used scaling fitting inhomogenous L-curves. '' cellType generating L curve. '' cellType generating L curve. cores Number cores use parallel processing BiocParallel MulticoreParam SerialParam object. minLambda Minimum value density scaling fitting inhomogeneous L-curves. window window around regions 'square', 'convex' 'concave'. window.length tuning parameter controlling level concavity estimating concave windows. edgeCorrect logical indicating whether perform edge correction. includeZeroCells logical indicating whether include cells zero counts pairwise association calculation.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"Statistic pairwise L-curve single image.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"","code":"data(\"diabetesData\") # Subset by imageID for fast example selected_cells <- diabetesData[   , SummarizedExperiment::colData(diabetesData)$imageID == \"A09\" ] pairAssoc <- getPairwise(selected_cells) #> Error in FUN(X[[i]], ...): object 'selected_cells' not found"},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":null,"dir":"Reference","previous_headings":"","what":"Get proportions from a SummarizedExperiment. — getProp","title":"Get proportions from a SummarizedExperiment. — getProp","text":"Get proportions SummarizedExperiment.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get proportions from a SummarizedExperiment. — getProp","text":"","code":"getProp(cells, feature = \"cellType\", imageID = \"imageID\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get proportions from a SummarizedExperiment. — getProp","text":"cells SingleCellExperiment, SpatialExperiment data.frame. feature feature interest imageID imageID's","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get proportions from a SummarizedExperiment. — getProp","text":"Proportions","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get proportions from a SummarizedExperiment. — getProp","text":"","code":"data(\"diabetesData\") prop <- getProp(diabetesData)"},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots result of signifPlot. — signifPlot","title":"Plots result of signifPlot. — signifPlot","text":"Plots result signifPlot.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots result of signifPlot. — signifPlot","text":"","code":"signifPlot(   results,   fdr = FALSE,   type = \"bubble\",   breaks = NULL,   comparisonGroup = NULL,   colours = c(\"#4575B4\", \"white\", \"#D73027\"),   marksToPlot = NULL,   cutoff = 0.05,   contextColours = NULL,   contextLabels = waiver() )"},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots result of signifPlot. — signifPlot","text":"results spicy results object fdr TRUE FDR correction used. type Whether make bubble plot heatmap. Note: survival results bubble plot used. breaks Vector 3 numbers giving breaks used legend. first number minimum, second maximum, third number breaks. comparisonGroup string specifying name outcome group compare base group. colours Vector colours use colour legend. marksToPlot Vector marks include plot. cutoff significance threshold circles bubble plot. contextColours Used Kontextual results. named list specifying colours context. default Tableau colour palette used. contextLabels Used Kontextual results. named list change default labels context.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots result of signifPlot. — signifPlot","text":"ggplot pheatmap object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots result of signifPlot. — signifPlot","text":"","code":"data(spicyTest)  p <- signifPlot(spicyTest, breaks = c(-3, 3, 0.5)) # plot includes unicode characters, do not use default pdf device ggplot2::ggsave(p, filename = tempfile(), device = cairo_pdf) #> Saving 7 x 7 in image"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"Performs spatial tests spatial cytometry data.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"","code":"spicy(   cells,   condition,   subject = NULL,   covariates = NULL,   imageID = \"imageID\",   cellType = \"cellType\",   spatialCoords = c(\"x\", \"y\"),   Rs = NULL,   sigma = NULL,   from = NULL,   to = NULL,   alternateResult = NULL,   cores = 1,   minLambda = 0.05,   weights = TRUE,   weightsByPair = FALSE,   weightFactor = 1,   window = \"convex\",   window.length = NULL,   edgeCorrect = TRUE,   includeZeroCells = FALSE,   BPPARAM = BiocParallel::SerialParam(),   imageIDCol = imageID,   cellTypeCol = cellType,   spatialCoordCols = spatialCoords,   nCores = cores,   verbose = FALSE,   ... )"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"cells SummarizedExperiment data frame contains least  variables x y, giving location coordinates cell, cellType. condition character specifying column contains condition `Surv` objects. subject Vector subject IDs corresponding image cells data frame. covariates Vector covariate names included mixed effects model fixed effects. imageID name imageID column using SingleCellExperiment SpatialExperiment. cellType name cellType column using SingleCellExperiment SpatialExperiment. spatialCoords names spatialCoords column using SingleCellExperiment. Rs vector radii measures association calculated . sigma numeric variable used scaling fitting inhomogenous L-curves. vector cell types like compare vector. vector cell types like compare vector. alternateResult pairwise association statistic combination celltypes image. cores Number cores use parallel processing BiocParallel MulticoreParam SerialParam object. minLambda Minimum value density scaling fitting inhomogeneous L-curves. weights logical indicating whether include weights based cell counts. weightsByPair logical indicating whether weights calculated cell type pair. weightFactor numeric controls convexity weight function. window window around regions 'square', 'convex' 'concave'. window.length tuning parameter controlling level concavity estimating concave windows. edgeCorrect logical indicating whether perform edge correction. includeZeroCells logical indicating whether include cells zero counts pairwise association calculation. verbose logical indicating whether output messages. ... options","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"Data frame p-values.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"","code":"data(\"diabetesData\")  # Test with random effect for patient on a pairwise combination of cell # types. spicy(diabetesData,   condition = \"stage\", subject = \"case\",   from = \"Tc\", to = \"Th\" ) #> Skipping argument checks as `getPairwise()` is being called within `spicy()` #>  #> Number of cell type pairs: 1 #> Number of differentially localised cell type pairs:  #> [1] 0  # Test all pairwise combinations of cell types without random effect of # patient. if (FALSE) { # \\dontrun{ spicyTest <- spicy(diabetesData, condition = \"stage\", subject = \"case\") } # }  # Test all pairwise combination of cell types with random effect of patient. if (FALSE) { # \\dontrun{ spicy(diabetesData, condition = \"condition\", subject = \"subject\") } # }"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"Plots boxplot specified cell-cell relationship","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"","code":"spicyBoxPlot(results, from = NULL, to = NULL, rank = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"results Data frame obtained spicy. Cell type like compare cell type. Cell type like compare cell type. rank Ranking cell type terms p-value, smaller p-value higher rank.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"ggplot2 boxplot","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"","code":"data(spicyTest)  spicyBoxPlot(spicyTest,              rank = 1) #> Warning: Removed 53 rows containing non-finite outside the scale range #> (`stat_boxplot()`)."},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Results from spicy for diabetesData — spicyTest","title":"Results from spicy for diabetesData — spicyTest","text":"Results call: spicyTest <- spicy(diabetesData,                    condition = \"condition\",                    subject = \"subject\")","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results from spicy for diabetesData — spicyTest","text":"","code":"data(\"spicyTest\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results from spicy for diabetesData — spicyTest","text":"spicyTest spicy object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots survival results from spicy. — survBubble","title":"Plots survival results from spicy. — survBubble","text":"Plots survival results spicy.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots survival results from spicy. — survBubble","text":"","code":"survBubble(   result,   fdr = FALSE,   cutoff = 0.05,   colourGradient = c(\"#4575B4\", \"white\", \"#D73027\"),   marksToPlot = NULL,   contextColours = NULL,   contextLabels = waiver() )"},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots survival results from spicy. — survBubble","text":"result spicyResults object contains survival results. fdr TRUE FDR correction used. cutoff Significance threshold circles bubble plot. colourGradient vector colours, used define low, medium, high values colour scale. marksToPlot Vector marks include bubble plot. contextColours Used Kontextual results. named list specifying colours context. default Tableau colour palette used. contextLabels Used Kontextual results. named list change default labels context.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots survival results from spicy. — survBubble","text":"ggplot object.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":null,"dir":"Reference","previous_headings":"","what":"A table of the significant results from spicy tests — topPairs","title":"A table of the significant results from spicy tests — topPairs","text":"table significant results spicy tests","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A table of the significant results from spicy tests — topPairs","text":"","code":"topPairs(x, coef = NULL, n = 10, adj = \"fdr\", cutoff = NULL, figures = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A table of the significant results from spicy tests — topPairs","text":"x output spicy. coef coefficient list. n Extract top n significant pairs. adj p-value adjustment method use, argument p.adjust(). cutoff p-value threshold extract significant pairs. figures Round `figures` significant figures.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A table of the significant results from spicy tests — topPairs","text":"data.frame","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A table of the significant results from spicy tests — topPairs","text":"","code":"data(spicyTest) topPairs(spicyTest) #>                          intercept coefficient      p.value adj.pvalue #> beta__delta           1.815458e+02  -48.735693 0.0005033247 0.07169649 #> delta__beta           1.817943e+02  -48.166076 0.0005601288 0.07169649 #> B__unknown           -3.606296e-15   11.770938 0.0052338392 0.42051606 #> delta__delta          2.089550e+02  -52.061196 0.0125129422 0.42051606 #> unknown__macrophage   1.007337e+01  -15.826919 0.0207410908 0.42051606 #> unknown__B           -5.161932e-15   12.142848 0.0225855419 0.42051606 #> macrophage__unknown   1.004424e+01  -14.471666 0.0244668075 0.42051606 #> B__Th                 1.571479e-15   26.847934 0.0245039854 0.42051606 #> otherimmune__naiveTc -9.292508e+00   33.584755 0.0255812944 0.42051606 #> ductal__ductal        1.481580e+01   -8.632569 0.0266935703 0.42051606 #>                             from         to #> beta__delta                 beta      delta #> delta__beta                delta       beta #> B__unknown                     B    unknown #> delta__delta               delta      delta #> unknown__macrophage      unknown macrophage #> unknown__B               unknown          B #> macrophage__unknown   macrophage    unknown #> B__Th                          B         Th #> otherimmune__naiveTc otherimmune    naiveTc #> ductal__ductal            ductal     ductal"}]
+[{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"","code":"if (!require(\"BiocManager\")) {   install.packages(\"BiocManager\") } BiocManager::install(\"spicyR\") # load required packages library(spicyR) library(ggplot2) library(SpatialExperiment) library(SpatialDatasets) library(imcRtools) library(dplyr) library(survival)"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"overview","dir":"Articles","previous_headings":"","what":"Overview","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"guide provides step--step instructions apply linear model multiple segmented labelled images assess localisation different cell types changes across different disease conditions.","code":""},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"example-data","dir":"Articles","previous_headings":"","what":"Example data","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"use Keren et al. (2018) breast cancer dataset compare spatial distribution immune cells individuals different levels tumour infiltration (cold compartmentalised). data stored SpatialExperiment object contains single-cell spatial data 41 images. cell types dataset includes 11 immune cell types (double negative CD3 T cells, CD4 T cells, B cells, monocytes, macrophages, CD8 T cells, neutrophils, natural killer cells, dendritic cells, regulatory T cells), 2 structural cell types (endothelial, mesenchymal), 2 tumour cell types (keratin+ tumour, tumour) one unidentified category.","code":"kerenSPE <- SpatialDatasets::spe_Keren_2018()"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"linear-modelling","dir":"Articles","previous_headings":"","what":"Linear modelling","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"investigate changes localisation two different cell types, measure level localisation two cell types modelling L-function. L-function variance-stabilised K-function given equation Liĵ(r)=Kiĵ(r)π \\widehat{L_{ij}} (r) = \\sqrt{\\frac{\\widehat{K_{ij}}(r)}{\\pi}} Kiĵ\\widehat{K_{ij}} defined Kiĵ(r)=|W|ninj∑ni∑nj1{dij≤r}eij(r) \\widehat{K_{ij}} (r) = \\frac{|W|}{n_i n_j} \\sum_{n_i} \\sum_{n_j} 1 \\{d_{ij} \\leq r \\} e_{ij} (r) Kiĵ\\widehat{K_{ij}} summarises degree co-localisation cell type jj cell type ii, nin_i njn_j number cells type ii jj, |W||W| image area, dijd_{ij} distance two cells eij(r)e_{ij} (r) edge correcting factor. Specifically, mean difference experimental function theoretical function used measure level localisation, defined u=∑r′=rminrmaxL̂ij,Experimental(r′)−L̂ij,Poisson(r′) u = \\sum_{r' = r_{\\text{min}}}^{r_{\\text{max}}} \\widehat L_{ij, \\text{Experimental}} (r') - \\widehat L_{ij, \\text{Poisson}} (r') uu sum taken discrete range rr rminr_{\\text{min}} rmaxr_{\\text{max}}. Differences statistic uu two conditions modelled using weighted linear model.","code":""},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"test-for-change-in-localisation-for-a-specific-pair-of-cells","dir":"Articles","previous_headings":"Linear modelling","what":"Test for change in localisation for a specific pair of cells","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"Firstly, can test whether one cell type tends localised another cell type one condition compared . can done using spicy() function, specify condition parameter. example, want see whether neutrophils () tend found around CD8 T cells () compartmentalised tumours compared cold tumours. Given 3 conditions, can specify desired conditions setting order condition factor. spicy choose first level factor base condition second level comparison condition. spicy also naturally coerce condition column factor already factor. column containing cell type annotations can specified using cellType argument. default, spicy uses column named cellType SpatialExperiment object. obtain spicy object details results modelling performed. topPairs() function can used obtain associated coefficients p-value. coefficient spicyTestPair positive, find neutrophils significantly likely found near CD8 T cells compartmentalised tumours group compared cold tumour group.","code":"spicyTestPair <- spicy(   kerenSPE,   condition = \"tumour_type\",   from = \"CD8_T_cell\",   to = \"Neutrophils\" )  topPairs(spicyTestPair) #>                         intercept coefficient      p.value   adj.pvalue #> CD8_T_cell__Neutrophils  -109.081    112.0185 2.166646e-05 2.166646e-05 #>                               from          to #> CD8_T_cell__Neutrophils CD8_T_cell Neutrophils"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"test-for-change-in-localisation-for-all-pairwise-cell-combinations","dir":"Articles","previous_headings":"Linear modelling","what":"Test for change in localisation for all pairwise cell combinations","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"can perform pairwise combinations cell types excluding parameters spicy(). , obtain spicy object outlines result linear models performed pairwise combination cell types. can also examine L-function metrics individual images using convenient bind() function spicyTest results object. results can represented bubble plot using signifPlot() function.  , can observe significant relationships occur macrophages double negative CD3 T cells, suggesting two cell types far dispersed compartmentalised tumours compared cold tumours. examine specific cell type-cell type relationship detail, can use spicyBoxplot() specify either = \"Macrophages\" = \"dn_T_CD3\" rank = 1.","code":"spicyTest <- spicy(   kerenSPE,   condition = \"tumour_type\" )  topPairs(spicyTest) #>                             intercept coefficient      p.value   adj.pvalue #> Macrophages__dn_T_CD3       56.446064   -50.08474 1.080273e-07 3.035568e-05 #> dn_T_CD3__Macrophages       54.987151   -48.38664 2.194018e-07 3.082595e-05 #> Macrophages__DC_or_Mono     73.239404   -59.90361 5.224660e-06 4.893765e-04 #> DC_or_Mono__Macrophages     71.777087   -58.46833 7.431172e-06 5.220399e-04 #> dn_T_CD3__dn_T_CD3         -63.786032   100.61010 2.878804e-05 1.208706e-03 #> Neutrophils__dn_T_CD3      -63.141840    69.64356 2.891872e-05 1.208706e-03 #> dn_T_CD3__Neutrophils      -63.133725    70.15508 3.011012e-05 1.208706e-03 #> DC__Macrophages             96.893239   -92.55112 1.801300e-04 5.758129e-03 #> Macrophages__DC             96.896215   -93.25194 1.844241e-04 5.758129e-03 #> CD4_T_cell__Keratin_Tumour  -4.845037   -22.14995 2.834659e-04 7.409016e-03 #>                                   from             to #> Macrophages__dn_T_CD3      Macrophages       dn_T_CD3 #> dn_T_CD3__Macrophages         dn_T_CD3    Macrophages #> Macrophages__DC_or_Mono    Macrophages     DC_or_Mono #> DC_or_Mono__Macrophages     DC_or_Mono    Macrophages #> dn_T_CD3__dn_T_CD3            dn_T_CD3       dn_T_CD3 #> Neutrophils__dn_T_CD3      Neutrophils       dn_T_CD3 #> dn_T_CD3__Neutrophils         dn_T_CD3    Neutrophils #> DC__Macrophages                     DC    Macrophages #> Macrophages__DC            Macrophages             DC #> CD4_T_cell__Keratin_Tumour  CD4_T_cell Keratin_Tumour bind(spicyTest)[1:5, 1:5] #>   imageID         condition Keratin_Tumour__Keratin_Tumour #> 1       1             mixed                      -2.300602 #> 2       2             mixed                      -1.989699 #> 3       3 compartmentalised                      11.373530 #> 4       4 compartmentalised                      33.931133 #> 5       5 compartmentalised                      17.922818 #>   dn_T_CD3__Keratin_Tumour B_cell__Keratin_Tumour #> 1                -5.298543             -20.827279 #> 2               -16.020022               3.025815 #> 3               -21.857447             -24.962913 #> 4               -36.438476             -40.470221 #> 5               -20.816783             -38.138076 signifPlot(   spicyTest,   breaks = c(-3, 3, 1),   marksToPlot = c(\"Macrophages\", \"DC_or_Mono\", \"dn_T_CD3\", \"Neutrophils\",                   \"CD8_T_cell\", \"Keratin_Tumour\") ) spicyBoxPlot(results = spicyTest,               # from = \"Macrophages\",              # to = \"dn_T_CD3\"              rank = 1)"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"linear-modelling-for-custom-metrics","dir":"Articles","previous_headings":"","what":"Linear modelling for custom metrics","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicyR can also applied custom distance abundance metrics. kNN interactions graph can generated function buildSpatialGraph imcRtools package. generates colPairs object inside SpatialExperiment object. spicyR provides function convPairs converting colPairs object abundance matrix calculating average number nearby cells types every cell type given k. example, exists average 5 neutrophils every macrophage image 1, column Neutrophil__Macrophage value 5 image 1. custom distance abundance metrics can included analysis alternateResult parameter. Statial package contains custom distance metrics can used spicy.","code":"kerenSPE <- imcRtools::buildSpatialGraph(kerenSPE,                                           img_id = \"imageID\",                                           type = \"knn\", k = 20,                                         coords = c(\"x\", \"y\"))  pairAbundances <- convPairs(kerenSPE,                   colPair = \"knn_interaction_graph\")  head(pairAbundances[\"B_cell__B_cell\"]) #>    B_cell__B_cell #> 1      12.7349608 #> 10      0.2777778 #> 11      0.0000000 #> 12      1.3333333 #> 13      1.2200957 #> 14      0.0000000 spicyTestColPairs <- spicy(   kerenSPE,   condition = \"tumour_type\",   alternateResult = pairAbundances,   weights = FALSE )  topPairs(spicyTestColPairs) #>                            intercept coefficient     p.value adj.pvalue #> CD8_T_cell__Neutrophils  0.833333333  -0.7592968 0.002645466  0.3291833 #> B_cell__Tumour           0.001937984   0.2602822 0.004872664  0.3291833 #> Other_Immune__NK         0.012698413   0.2612881 0.005673068  0.3291833 #> Unidentified__CD8_T_cell 0.106626794   0.6387339 0.005906526  0.3291833 #> dn_T_CD3__NK             0.004242424   0.2148797 0.006317829  0.3291833 #> CD4_T_cell__Neutrophils  0.036213602   0.2947696 0.007902670  0.3291833 #> Tregs__CD4_T_cell        0.128876212   0.5726201 0.010207087  0.3291833 #> Endothelial__DC          0.008771930   0.3008523 0.011189533  0.3291833 #> Tumour__Neutrophils      0.021638939   0.2529045 0.011388850  0.3291833 #> Mesenchymal__Neutrophils 0.004504505   0.2494301 0.012761315  0.3291833 #>                                  from          to #> CD8_T_cell__Neutrophils    CD8_T_cell Neutrophils #> B_cell__Tumour                 B_cell      Tumour #> Other_Immune__NK         Other_Immune          NK #> Unidentified__CD8_T_cell Unidentified  CD8_T_cell #> dn_T_CD3__NK                 dn_T_CD3          NK #> CD4_T_cell__Neutrophils    CD4_T_cell Neutrophils #> Tregs__CD4_T_cell               Tregs  CD4_T_cell #> Endothelial__DC           Endothelial          DC #> Tumour__Neutrophils            Tumour Neutrophils #> Mesenchymal__Neutrophils  Mesenchymal Neutrophils signifPlot(   spicyTestColPairs,   breaks = c(-3, 3, 1),   marksToPlot = c(\"Macrophages\", \"dn_T_CD3\", \"CD4_T_cell\",                    \"B_cell\", \"DC_or_Mono\", \"Neutrophils\", \"CD8_T_cell\") )"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"performing-survival-analysis","dir":"Articles","previous_headings":"","what":"Performing survival analysis","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicy can also used perform survival analysis asses whether changes co-localisation cell types associated survival probability. spicy requires SingleCellExperiment object used contain column called survival Surv object. can perform survival analysis using spicy function specifying condition = \"survival\". can access corresponding coefficients p-values accessing survivalResults slot spicy results object.","code":"kerenSPE$event = 1 - kerenSPE$Censored kerenSPE$survival = Surv(kerenSPE$`Survival_days_capped*`, kerenSPE$event) # Running survival analysis spicySurvival = spicy(kerenSPE,                       condition = \"survival\")  # top 10 significant pairs head(spicySurvival$survivalResults, 10) #> # A tibble: 10 × 4 #>    test                       coef se.coef    p.value #>    <chr>                     <dbl>   <dbl>      <dbl> #>  1 Other_Immune__Tregs     0.0236  0.00866 0.00000893 #>  2 CD4_T_cell__Tregs       0.0177  0.00685 0.0000124  #>  3 Tregs__Other_Immune     0.0237  0.00873 0.0000126  #>  4 Tregs__CD4_T_cell       0.0171  0.00676 0.0000285  #>  5 CD8_T_cell__CD8_T_cell  0.00605 0.00272 0.000332   #>  6 Tumour__CD8_T_cell     -0.0305  0.0114  0.000617   #>  7 CD8_T_cell__Tumour     -0.0305  0.0116  0.000721   #>  8 CD4_T_cell__dn_T_CD3    0.00845 0.00353 0.000794   #>  9 dn_T_CD3__CD4_T_cell    0.00840 0.00353 0.000937   #> 10 DC__Other_Immune       -0.0289  0.0123  0.00103"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"accounting-for-tissue-inhomogeneity","dir":"Articles","previous_headings":"","what":"Accounting for tissue inhomogeneity","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicy function can also account tissue inhomogeneity avoid false positives negatives. can done setting sigma = parameter within spicy function. default, sigma set NULL, spicy assumes homogeneous tissue structure. example, examine L-function Keratin_Tumour__Neutrophils sigma = NULL Rs = 100, value positive, indicating attraction two cell types. specify sigma = 20 re-calculate L-function, indicates relationship Keratin_Tumour Neutrophils, .e., major attraction dispersion, now takes account tissue inhomogeneity.  Plotting image 24 shows supposed co-localisation occurs due dense cluster cells near top image.","code":"# filter SPE object to obtain image 24 data kerenSubset = kerenSPE[, colData(kerenSPE)$imageID == \"24\"]  pairwiseAssoc = getPairwise(kerenSubset,                              sigma = NULL,                              Rs = 100) |>   as.data.frame()  pairwiseAssoc[[\"Keratin_Tumour__Neutrophils\"]] #> [1] 10.88892 pairwiseAssoc = getPairwise(kerenSubset,                              sigma = 20,                              Rs = 100) |>   as.data.frame()  pairwiseAssoc[[\"Keratin_Tumour__Neutrophils\"]] #> [1] 0.9024836 # obtain colData for image 24 cData = colData(kerenSPE) |> as.data.frame() |>            dplyr::filter(imageID == \"24\")  # obtain cells present in image 24 coords = spatialCoords(kerenSPE) |> as.data.frame() coords$cellID = rownames(coords) coords = coords |> dplyr::filter(cellID %in% cData$CellID)  cData$X = coords$x cData$Y = coords$y  cData = cData |>    dplyr::mutate(cellTypeNew = ifelse(cellType %in% c(\"Keratin_Tumour\", \"Neutrophils\"),                                       cellType, \"Other\"))   pal = setNames(c(\"#d6b11c\", \"#850f07\"),                  c(\"Keratin_Tumour\", \"Neutrophils\"))  ggplot() +     stat_density_2d(data = cData, aes(x = X, y = Y, fill = after_stat(density)),                      geom = \"raster\",                      contour = FALSE) +     geom_point(data = cData |> filter(cellType != \"Other\"),                aes(x = X, y = Y, colour = cellTypeNew), size = 1) +     scale_color_manual(values = pal) +     scale_fill_distiller(palette = \"Blues\", direction = 1) +     theme_classic() +     labs(title = \"image ID: 24\")"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"mixed-effects-modelling","dir":"Articles","previous_headings":"","what":"Mixed effects modelling","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"spicyR supports mixed effects modelling multiple images obtained subject. case, subject treated random effect condition treated fixed effect. perform mixed effects modelling, can specify subject parameter spicy function.","code":"spicyMixedTest <- spicy(   diabetesData,   condition = \"stage\",   subject = \"case\" )"},{"path":"https://sydneybiox.github.io/spicyR/articles/spicyR.html","id":"references","dir":"Articles","previous_headings":"","what":"References","title":"Spatial Linear and Mixed-Effects Modelling with spicyR","text":"","code":"sessionInfo() #> R version 4.4.2 (2024-10-31) #> Platform: x86_64-pc-linux-gnu #> Running under: Ubuntu 22.04.5 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 #>  #> locale: #>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        #>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    #>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           #> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    #>  #> time zone: UTC #> tzcode source: system (glibc) #>  #> attached base packages: #> [1] stats4    stats     graphics  grDevices utils     datasets  methods   #> [8] base      #>  #> other attached packages: #>  [1] survival_3.7-0              dplyr_1.1.4                 #>  [3] imcRtools_1.12.0            SpatialDatasets_1.4.0       #>  [5] ExperimentHub_2.14.0        AnnotationHub_3.14.0        #>  [7] BiocFileCache_2.14.0        dbplyr_2.5.0                #>  [9] SpatialExperiment_1.16.0    SingleCellExperiment_1.28.1 #> [11] SummarizedExperiment_1.36.0 Biobase_2.66.0              #> [13] GenomicRanges_1.58.0        GenomeInfoDb_1.42.0         #> [15] IRanges_2.40.0              S4Vectors_0.44.0            #> [17] BiocGenerics_0.52.0         MatrixGenerics_1.18.0       #> [19] matrixStats_1.4.1           ggplot2_3.5.1               #> [21] spicyR_1.19.2               BiocStyle_2.34.0            #>  #> loaded via a namespace (and not attached): #>   [1] fs_1.6.5                    spatstat.sparse_3.1-0       #>   [3] bitops_1.0-9                sf_1.0-19                   #>   [5] EBImage_4.48.0              httr_1.4.7                  #>   [7] RColorBrewer_1.1-3          numDeriv_2016.8-1.1         #>   [9] tools_4.4.2                 backports_1.5.0             #>  [11] utf8_1.2.4                  R6_2.5.1                    #>  [13] DT_0.33                     HDF5Array_1.34.0            #>  [15] mgcv_1.9-1                  rhdf5filters_1.18.0         #>  [17] withr_3.0.2                 sp_2.1-4                    #>  [19] gridExtra_2.3               coxme_2.2-22                #>  [21] ClassifyR_3.10.0            cli_3.6.3                   #>  [23] textshaping_0.4.0           spatstat.explore_3.3-3      #>  [25] labeling_0.4.3              sass_0.4.9                  #>  [27] nnls_1.6                    spatstat.data_3.1-2         #>  [29] readr_2.1.5                 proxy_0.4-27                #>  [31] pkgdown_2.1.1               systemfonts_1.1.0           #>  [33] ggupset_0.4.0               svglite_2.1.3               #>  [35] RSQLite_2.3.7               generics_0.1.3              #>  [37] vroom_1.6.5                 spatstat.random_3.3-2       #>  [39] car_3.1-3                   scam_1.2-17                 #>  [41] Matrix_1.7-1                ggbeeswarm_0.7.2            #>  [43] fansi_1.0.6                 abind_1.4-8                 #>  [45] terra_1.7-83                lifecycle_1.0.4             #>  [47] yaml_2.3.10                 carData_3.0-5               #>  [49] rhdf5_2.50.0                SparseArray_1.6.0           #>  [51] grid_4.4.2                  blob_1.2.4                  #>  [53] promises_1.3.0              crayon_1.5.3                #>  [55] bdsmatrix_1.3-7             shinydashboard_0.7.2        #>  [57] lattice_0.22-6              beachmat_2.22.0             #>  [59] KEGGREST_1.46.0             magick_2.8.5                #>  [61] cytomapper_1.18.0           pillar_1.9.0                #>  [63] knitr_1.49                  RTriangle_1.6-0.14          #>  [65] rjson_0.2.23                boot_1.3-31                 #>  [67] codetools_0.2-20            glue_1.8.0                  #>  [69] spatstat.univar_3.1-1       data.table_1.16.2           #>  [71] MultiAssayExperiment_1.32.0 vctrs_0.6.5                 #>  [73] png_0.1-8                   gtable_0.3.6                #>  [75] cachem_1.1.0                xfun_0.49                   #>  [77] S4Arrays_1.6.0              mime_0.12                   #>  [79] tidygraph_1.3.1             pheatmap_1.0.12             #>  [81] units_0.8-5                 nlme_3.1-166                #>  [83] bit64_4.5.2                 filelock_1.0.3              #>  [85] bslib_0.8.0                 svgPanZoom_0.3.4            #>  [87] KernSmooth_2.23-24          vipor_0.4.7                 #>  [89] colorspace_2.1-1            DBI_1.2.3                   #>  [91] raster_3.6-30               tidyselect_1.2.1            #>  [93] bit_4.5.0                   compiler_4.4.2              #>  [95] curl_6.0.0                  BiocNeighbors_2.0.0         #>  [97] desc_1.4.3                  DelayedArray_0.32.0         #>  [99] bookdown_0.41               scales_1.3.0                #> [101] classInt_0.4-10             distances_0.1.11            #> [103] rappdirs_0.3.3              tiff_0.1-12                 #> [105] stringr_1.5.1               digest_0.6.37               #> [107] goftest_1.2-3               fftwtools_0.9-11            #> [109] spatstat.utils_3.1-1        minqa_1.2.8                 #> [111] rmarkdown_2.29              XVector_0.46.0              #> [113] htmltools_0.5.8.1           pkgconfig_2.0.3             #> [115] jpeg_0.1-10                 lme4_1.1-35.5               #> [117] fastmap_1.2.0               rlang_1.1.4                 #> [119] htmlwidgets_1.6.4           ggthemes_5.1.0              #> [121] UCSC.utils_1.2.0            shiny_1.9.1                 #> [123] ggh4x_0.2.8                 farver_2.1.2                #> [125] jquerylib_0.1.4             jsonlite_1.8.9              #> [127] BiocParallel_1.40.0         RCurl_1.98-1.16             #> [129] magrittr_2.0.3              scuttle_1.16.0              #> [131] Formula_1.2-5               GenomeInfoDbData_1.2.13     #> [133] Rhdf5lib_1.28.0             munsell_0.5.1               #> [135] Rcpp_1.0.13-1               ggnewscale_0.5.0            #> [137] viridis_0.6.5               stringi_1.8.4               #> [139] ggraph_2.2.1                zlibbioc_1.52.0             #> [141] MASS_7.3-61                 plyr_1.8.9                  #> [143] parallel_4.4.2              ggrepel_0.9.6               #> [145] deldir_2.0-4                Biostrings_2.74.0           #> [147] graphlayouts_1.2.0          splines_4.4.2               #> [149] tensor_1.5                  hms_1.1.3                   #> [151] locfit_1.5-9.10             igraph_2.1.1                #> [153] ggpubr_0.6.0                spatstat.geom_3.3-3         #> [155] ggsignif_0.6.4              reshape2_1.4.4              #> [157] BiocVersion_3.20.0          evaluate_1.0.1              #> [159] BiocManager_1.30.25         tzdb_0.4.0                  #> [161] nloptr_2.1.1                tweenr_2.0.3                #> [163] httpuv_1.6.15               tidyr_1.3.1                 #> [165] purrr_1.0.2                 polyclip_1.10-7             #> [167] ggforce_0.4.2               broom_1.0.7                 #> [169] xtable_1.8-4                e1071_1.7-16                #> [171] rstatix_0.7.2               later_1.3.2                 #> [173] class_7.3-22                viridisLite_0.4.2           #> [175] ragg_1.3.3                  tibble_3.2.1                #> [177] lmerTest_3.1-3              memoise_2.0.1               #> [179] beeswarm_0.4.0              AnnotationDbi_1.68.0        #> [181] concaveman_1.1.0"},{"path":"https://sydneybiox.github.io/spicyR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Nicolas Canete. Author. Ellis Patrick. Author, maintainer. Nicholas Robertson. Contributor. Alex Qin. Contributor. Farhan Ameen. Contributor. Shreya Rao. Contributor.","code":""},{"path":"https://sydneybiox.github.io/spicyR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Canete N, Iyengar S, Ormerod J, Baharlou H, Harman , Patrick E (2022). “spicyR: spatial analysis situ cytometry data R.” Bioinformatics, 38(11), 3099–3105. doi:10.1093/bioinformatics/btac268, https://doi.org/10.1093/bioinformatics/btac268.","code":"@Article{,   title = {{spicyR: spatial analysis of in situ cytometry data in R}},   author = {Nicolas Canete and Sourish Iyengar and John Ormerod and Heeva Baharlou and Andrew Harman and Ellis Patrick},   year = {2022},   volume = {38},   number = {11},   pages = {3099–3105},   journal = {Bioinformatics},   doi = {10.1093/bioinformatics/btac268},   url = {https://doi.org/10.1093/bioinformatics/btac268}, }"},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"spicyr","dir":"","previous_headings":"","what":"Spatial analysis of in situ cytometry data","title":"Spatial analysis of in situ cytometry data","text":"Spatial analysis situ cytometry data.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Spatial analysis of in situ cytometry data","text":"spicyR package provides framework performing inference changes spatial relationships pairs cell types cell-resolution spatial omics technologies. spicyR consists three primary steps: () summarizing degree spatial localization pairs cell types image; (ii) modelling variability localization summary statistics function cell counts (iii) testing changes spatial localizations associated response variable.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Spatial analysis of in situ cytometry data","text":"Bioconductor release version, run following. like --date features, install recent development version.","code":"if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  BiocManager::install(\"spicyR\") # Install the development version from Bioconductor: if (!require(\"BiocManager\", quietly = TRUE))     install.packages(\"BiocManager\")  # The following initializes usage of Bioc devel # This will update all your Bioconductor packages to devel version BiocManager::install(version='devel')  BiocManager::install(\"spicyR\")  # Otherwise install the development version from GitHub: # install.packages(\"devtools\") devtools::install_github(\"ellispatrick/spicyR\") library(spicyR)"},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"submitting-an-issue-or-feature-request","dir":"","previous_headings":"","what":"Submitting an issue or feature request","title":"Spatial analysis of in situ cytometry data","text":"spicyR still active development. greatly appreciate feedback related package. R package related issues raised . general questions feedback, please contact us directly via ellis.patrick@sydney.edu.au.","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"authors","dir":"","previous_headings":"","what":"Authors","title":"Spatial analysis of in situ cytometry data","text":"Nicolas Canete Ellis Patrick - @TheEllisPatrick","code":""},{"path":"https://sydneybiox.github.io/spicyR/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Spatial analysis of in situ cytometry data","text":"Nicolas P Canete, Sourish S Iyengar, John T Ormerod, Heeva Baharlou, Andrew N Harman, Ellis Patrick, spicyR: spatial analysis situ cytometry data R, Bioinformatics, Volume 38, Issue 11, 1 June 2022, Pages 3099–3105, https://doi.org/10.1093/bioinformatics/btac268","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":null,"dir":"Reference","previous_headings":"","what":"Produces a dataframe showing L-function metric for each imageID entry. — bind","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"Produces dataframe showing L-function metric imageID entry.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"","code":"bind(results, pairName = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"results Spicy test result obtained spicy. pairName string specifying pairwise interaction interest. NULL, pairwise interactions shown.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"data.frame containing colData related results.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/bind.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Produces a dataframe showing L-function metric for each imageID entry. — bind","text":"","code":"data(spicyTest) df <- bind(spicyTest)"},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"Perform simple wilcoxon-rank-sum test t-test columns data frame","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"","code":"colTest(df, condition, type = NULL, feature = NULL, imageID = \"imageID\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"df data.frame SingleCellExperiment, SpatialExperiment condition condition interest type type test, \"wilcox\", \"ttest\" \"survival\". feature Can used calculate proportions feature image imageID imageID's presenting SingleCellExperiment","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"Proportions","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/colTest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a simple wilcoxon-rank-sum test or t-test on the columns of a data frame — colTest","text":"","code":"# Test for an association with long-duration diabetes # This is clearly ignoring the repeated measures... data(\"diabetesData\") diabetesData <- spicyR:::.format_data(   diabetesData, \"imageID\", \"cellType\", c(\"x\", \"y\"), FALSE ) props <- getProp(diabetesData) condition <- spicyR:::getImagePheno(diabetesData)$stage names(condition) <- spicyR:::getImagePheno(diabetesData)$imageID condition <- condition[condition %in% c(\"Long-duration\", \"Onset\")] test <- colTest(props[names(condition), ], condition)"},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":null,"dir":"Reference","previous_headings":"","what":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"Converts colPairs object abundance matrix based number nearby interactions every cell type.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"","code":"convPairs(cells, colPair, imageID = \"imageID\", cellType = \"cellType\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"cells SingleCellExperiment contains objects colPairs slot. colPair name object colPairs slot dataframe constructed . imageID image ID using SingleCellExperiment. cellType cell type using SingleCellExperiment.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"Matrix abundances","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/convPairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Converts colPairs object into an abundance matrix based on number of nearby interactions for every cell type. — convPairs","text":"","code":"data(\"diabetesData\") images <- c(\"A09\", \"A11\", \"A16\", \"A17\") diabetesData <- diabetesData[   , SummarizedExperiment::colData(diabetesData)$imageID %in% images ]  diabetesData_SPE <- SpatialExperiment::SpatialExperiment(diabetesData,   colData = SummarizedExperiment::colData(diabetesData) ) SpatialExperiment::spatialCoords(diabetesData_SPE) <- data.frame(   SummarizedExperiment::colData(diabetesData_SPE)$x,   SummarizedExperiment::colData(diabetesData_SPE)$y ) |>   as.matrix()  SpatialExperiment::spatialCoordsNames(diabetesData_SPE) <- c(\"x\", \"y\")  diabetesData_SPE <- imcRtools::buildSpatialGraph(diabetesData_SPE,   img_id = \"imageID\",   type = \"knn\",   k = 20,   coords = c(\"x\", \"y\") ) #> 'sample_id's are duplicated across 'SpatialExperiment' objects to cbind; appending sample indices. #> The returned object is ordered by the 'imageID' entry.  pairAbundances <- convPairs(diabetesData_SPE,   colPair = \"knn_interaction_graph\" )"},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":null,"dir":"Reference","previous_headings":"","what":"Diabetes IMC data in SCE format. — diabetesData","title":"Diabetes IMC data in SCE format. — diabetesData","text":"subset Damond et al 2019 imaging mass cytometry dataset. data contains cells pancreatic islets individuals early onset diabetes healthy controls. object contains single-cell data 160 images 8 subjects, 20 images per subject.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diabetes IMC data in SCE format. — diabetesData","text":"","code":"data(\"diabetesData\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Diabetes IMC data in SCE format. — diabetesData","text":"diabetesData_SCE SingleCellExperiment object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/diabetesData.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diabetes IMC data in SCE format. — diabetesData","text":"Converted SingleCellExperiment format.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Get statistic from pairwise L curve of a single image. — getPairwise","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"Get statistic pairwise L curve single image.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"","code":"getPairwise(   cells,   imageID = \"imageID\",   cellType = \"cellType\",   spatialCoords = c(\"x\", \"y\"),   Rs = NULL,   sigma = NULL,   from = NULL,   to = NULL,   cores = 1,   minLambda = 0.05,   window = \"convex\",   window.length = NULL,   edgeCorrect = TRUE,   includeZeroCells = FALSE,   BPPARAM = BiocParallel::SerialParam(),   imageIDCol = imageID,   cellTypeCol = cellType,   spatialCoordCols = spatialCoords,   nCores = cores )"},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"cells SummarizedExperiment contains least variables x y, giving location coordinates cell, cellType. imageID name imageID column using SingleCellExperiment SpatialExperiment. cellType name cellType column using SingleCellExperiment SpatialExperiment. spatialCoords names spatialCoords column using SingleCellExperiment. Rs vector radii measures association calculated . sigma numeric variable used scaling fitting inhomogenous L-curves. '' cellType generating L curve. '' cellType generating L curve. cores Number cores use parallel processing BiocParallel MulticoreParam SerialParam object. minLambda Minimum value density scaling fitting inhomogeneous L-curves. window window around regions 'square', 'convex' 'concave'. window.length tuning parameter controlling level concavity estimating concave windows. edgeCorrect logical indicating whether perform edge correction. includeZeroCells logical indicating whether include cells zero counts pairwise association calculation.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"Statistic pairwise L-curve single image.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getPairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get statistic from pairwise L curve of a single image. — getPairwise","text":"","code":"data(\"diabetesData\") # Subset by imageID for fast example selected_cells <- diabetesData[   , SummarizedExperiment::colData(diabetesData)$imageID == \"A09\" ] pairAssoc <- getPairwise(selected_cells) #> Error in FUN(X[[i]], ...): object 'selected_cells' not found"},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":null,"dir":"Reference","previous_headings":"","what":"Get proportions from a SummarizedExperiment. — getProp","title":"Get proportions from a SummarizedExperiment. — getProp","text":"Get proportions SummarizedExperiment.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get proportions from a SummarizedExperiment. — getProp","text":"","code":"getProp(cells, feature = \"cellType\", imageID = \"imageID\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get proportions from a SummarizedExperiment. — getProp","text":"cells SingleCellExperiment, SpatialExperiment data.frame. feature feature interest imageID imageID's","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get proportions from a SummarizedExperiment. — getProp","text":"Proportions","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/getProp.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get proportions from a SummarizedExperiment. — getProp","text":"","code":"data(\"diabetesData\") prop <- getProp(diabetesData)"},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots result of signifPlot. — signifPlot","title":"Plots result of signifPlot. — signifPlot","text":"Plots result signifPlot.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots result of signifPlot. — signifPlot","text":"","code":"signifPlot(   results,   fdr = FALSE,   type = \"bubble\",   breaks = NULL,   comparisonGroup = NULL,   colours = c(\"#4575B4\", \"white\", \"#D73027\"),   marksToPlot = NULL,   cutoff = 0.05,   contextColours = NULL,   contextLabels = waiver() )"},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots result of signifPlot. — signifPlot","text":"results spicy results object fdr TRUE FDR correction used. type Whether make bubble plot heatmap. Note: survival results bubble plot used. breaks Vector 3 numbers giving breaks used legend. first number minimum, second maximum, third number breaks. comparisonGroup string specifying name outcome group compare base group. colours Vector colours use colour legend. marksToPlot Vector marks include plot. cutoff significance threshold circles bubble plot. contextColours Used Kontextual results. named list specifying colours context. default Tableau colour palette used. contextLabels Used Kontextual results. named list change default labels context.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots result of signifPlot. — signifPlot","text":"ggplot pheatmap object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/signifPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots result of signifPlot. — signifPlot","text":"","code":"data(spicyTest)  p <- signifPlot(spicyTest, breaks = c(-3, 3, 0.5)) # plot includes unicode characters, do not use default pdf device ggplot2::ggsave(p, filename = tempfile(), device = cairo_pdf) #> Saving 7 x 7 in image"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":null,"dir":"Reference","previous_headings":"","what":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"Performs spatial tests spatial cytometry data.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"","code":"spicy(   cells,   condition,   subject = NULL,   covariates = NULL,   imageID = \"imageID\",   cellType = \"cellType\",   spatialCoords = c(\"x\", \"y\"),   Rs = NULL,   sigma = NULL,   from = NULL,   to = NULL,   alternateResult = NULL,   cores = 1,   minLambda = 0.05,   weights = TRUE,   weightsByPair = FALSE,   weightFactor = 1,   window = \"convex\",   window.length = NULL,   edgeCorrect = TRUE,   includeZeroCells = FALSE,   BPPARAM = BiocParallel::SerialParam(),   imageIDCol = imageID,   cellTypeCol = cellType,   spatialCoordCols = spatialCoords,   nCores = cores,   verbose = FALSE,   ... )"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"cells SummarizedExperiment data frame contains least  variables x y, giving location coordinates cell, cellType. condition character specifying column contains condition `Surv` objects. subject Vector subject IDs corresponding image cells data frame. covariates Vector covariate names included mixed effects model fixed effects. imageID name imageID column using SingleCellExperiment SpatialExperiment. cellType name cellType column using SingleCellExperiment SpatialExperiment. spatialCoords names spatialCoords column using SingleCellExperiment. Rs vector radii measures association calculated . sigma numeric variable used scaling fitting inhomogenous L-curves. vector cell types like compare vector. vector cell types like compare vector. alternateResult pairwise association statistic combination celltypes image. cores Number cores use parallel processing BiocParallel MulticoreParam SerialParam object. minLambda Minimum value density scaling fitting inhomogeneous L-curves. weights logical indicating whether include weights based cell counts. weightsByPair logical indicating whether weights calculated cell type pair. weightFactor numeric controls convexity weight function. window window around regions 'square', 'convex' 'concave'. window.length tuning parameter controlling level concavity estimating concave windows. edgeCorrect logical indicating whether perform edge correction. includeZeroCells logical indicating whether include cells zero counts pairwise association calculation. verbose logical indicating whether output messages. ... options","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"Data frame p-values.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Performs spatial tests on spatial cytometry data. — SpicyResults-class","text":"","code":"data(\"diabetesData\")  # Test with random effect for patient on a pairwise combination of cell # types. spicy(diabetesData,   condition = \"stage\", subject = \"case\",   from = \"Tc\", to = \"Th\" ) #> Skipping argument checks as `getPairwise()` is being called within `spicy()` #>  #> Number of cell type pairs: 1 #> Number of differentially localised cell type pairs:  #> [1] 0  # Test all pairwise combinations of cell types without random effect of # patient. if (FALSE) { # \\dontrun{ spicyTest <- spicy(diabetesData, condition = \"stage\", subject = \"case\") } # }  # Test all pairwise combination of cell types with random effect of patient. if (FALSE) { # \\dontrun{ spicy(diabetesData, condition = \"condition\", subject = \"subject\") } # }"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"Plots boxplot specified cell-cell relationship","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"","code":"spicyBoxPlot(results, from = NULL, to = NULL, rank = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"results Data frame obtained spicy. Cell type like compare cell type. Cell type like compare cell type. rank Ranking cell type terms p-value, smaller p-value higher rank.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"ggplot2 boxplot","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyBoxPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots boxplot for a specified cell-cell relationship — spicyBoxPlot","text":"","code":"data(spicyTest)  spicyBoxPlot(spicyTest,              rank = 1) #> Warning: Removed 53 rows containing non-finite outside the scale range #> (`stat_boxplot()`)."},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":null,"dir":"Reference","previous_headings":"","what":"Results from spicy for diabetesData — spicyTest","title":"Results from spicy for diabetesData — spicyTest","text":"Results call: spicyTest <- spicy(diabetesData,                    condition = \"condition\",                    subject = \"subject\")","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Results from spicy for diabetesData — spicyTest","text":"","code":"data(\"spicyTest\")"},{"path":"https://sydneybiox.github.io/spicyR/reference/spicyTest.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Results from spicy for diabetesData — spicyTest","text":"spicyTest spicy object","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots survival results from spicy. — survBubble","title":"Plots survival results from spicy. — survBubble","text":"Plots survival results spicy.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots survival results from spicy. — survBubble","text":"","code":"survBubble(   result,   fdr = FALSE,   cutoff = 0.05,   colourGradient = c(\"#4575B4\", \"white\", \"#D73027\"),   marksToPlot = NULL,   contextColours = NULL,   contextLabels = waiver() )"},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots survival results from spicy. — survBubble","text":"result spicyResults object contains survival results. fdr TRUE FDR correction used. cutoff Significance threshold circles bubble plot. colourGradient vector colours, used define low, medium, high values colour scale. marksToPlot Vector marks include bubble plot. contextColours Used Kontextual results. named list specifying colours context. default Tableau colour palette used. contextLabels Used Kontextual results. named list change default labels context.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/survBubble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots survival results from spicy. — survBubble","text":"ggplot object.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":null,"dir":"Reference","previous_headings":"","what":"A table of the significant results from spicy tests — topPairs","title":"A table of the significant results from spicy tests — topPairs","text":"table significant results spicy tests","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A table of the significant results from spicy tests — topPairs","text":"","code":"topPairs(x, coef = NULL, n = 10, adj = \"fdr\", cutoff = NULL, figures = NULL)"},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A table of the significant results from spicy tests — topPairs","text":"x output spicy. coef coefficient list. n Extract top n significant pairs. adj p-value adjustment method use, argument p.adjust(). cutoff p-value threshold extract significant pairs. figures Round `figures` significant figures.","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A table of the significant results from spicy tests — topPairs","text":"data.frame","code":""},{"path":"https://sydneybiox.github.io/spicyR/reference/topPairs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A table of the significant results from spicy tests — topPairs","text":"","code":"data(spicyTest) topPairs(spicyTest) #>                          intercept coefficient      p.value adj.pvalue #> beta__delta           1.815458e+02  -48.735693 0.0005033247 0.07169649 #> delta__beta           1.817943e+02  -48.166076 0.0005601288 0.07169649 #> B__unknown           -3.606296e-15   11.770938 0.0052338392 0.42051606 #> delta__delta          2.089550e+02  -52.061196 0.0125129422 0.42051606 #> unknown__macrophage   1.007337e+01  -15.826919 0.0207410908 0.42051606 #> unknown__B           -5.161932e-15   12.142848 0.0225855419 0.42051606 #> macrophage__unknown   1.004424e+01  -14.471666 0.0244668075 0.42051606 #> B__Th                 1.571479e-15   26.847934 0.0245039854 0.42051606 #> otherimmune__naiveTc -9.292508e+00   33.584755 0.0255812944 0.42051606 #> ductal__ductal        1.481580e+01   -8.632569 0.0266935703 0.42051606 #>                             from         to #> beta__delta                 beta      delta #> delta__beta                delta       beta #> B__unknown                     B    unknown #> delta__delta               delta      delta #> unknown__macrophage      unknown macrophage #> unknown__B               unknown          B #> macrophage__unknown   macrophage    unknown #> B__Th                          B         Th #> otherimmune__naiveTc otherimmune    naiveTc #> ductal__ductal            ductal     ductal"}]